{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Transformer XL",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYV_dMVDxyc2"
      },
      "source": [
        "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/xl/experiment.ipynb)                    \n",
        "\n",
        "## Transformer XL\n",
        "\n",
        "This is an experiment training Shakespeare dataset with a Transformer XL model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AahG_i2y5tY9"
      },
      "source": [
        "Install the `labml-nn` package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCzmCrAIVg0L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a4a59ce-b300-4d9f-baee-15720d696773"
      },
      "source": [
        "!pip install labml-nn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting labml-nn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/eb/122e36372ca246eced93fbe380730c660cde63ac26dcb6440610bc8709dc/labml_nn-0.4.86-py3-none-any.whl (145kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 17.4MB/s \n",
            "\u001b[?25hCollecting labml>=0.4.97\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c6/e5/4f297474555a3e80b01c02861bdb90c7cd088235553563e8f3fb790bc5ee/labml-0.4.99-py3-none-any.whl (101kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 12.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from labml-nn) (1.19.5)\n",
            "Collecting labml-helpers>=0.4.74\n",
            "  Downloading https://files.pythonhosted.org/packages/71/40/c0b73ed57edbb05d98b5abbdc82d852e5efe5739898215274b423b97bddc/labml_helpers-0.4.74-py3-none-any.whl\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from labml-nn) (1.7.0+cu101)\n",
            "Collecting einops\n",
            "  Downloading https://files.pythonhosted.org/packages/5d/a0/9935e030634bf60ecd572c775f64ace82ceddf2f504a5fd3902438f07090/einops-0.3.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from labml>=0.4.97->labml-nn) (3.13)\n",
            "Collecting gitpython\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/cb/ec98155c501b68dcb11314c7992cd3df6dce193fd763084338a117967d53/GitPython-3.1.12-py3-none-any.whl (159kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 58.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.16.0)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.8)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (3.7.4.3)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.2MB/s \n",
            "\u001b[?25hCollecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
            "Installing collected packages: smmap, gitdb, gitpython, labml, labml-helpers, einops, labml-nn\n",
            "Successfully installed einops-0.3.0 gitdb-4.0.5 gitpython-3.1.12 labml-0.4.99 labml-helpers-0.4.74 labml-nn-0.4.86 smmap-3.0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SE2VUQ6L5zxI"
      },
      "source": [
        "Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hJXx_g0wS2C"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "from labml import experiment\n",
        "from labml.configs import option\n",
        "from labml_helpers.module import Module\n",
        "from labml_nn.transformers.xl.experiment import Configs"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lpggo0wM6qb-"
      },
      "source": [
        "Create an experiment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFcr9k-l4cAg"
      },
      "source": [
        "experiment.create(name=\"transformer_xl\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OnHLi626tJt"
      },
      "source": [
        "Initialize configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Piz0c5f44hRo"
      },
      "source": [
        "conf = Configs()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwMzCqpD6vkL"
      },
      "source": [
        "Set experiment configurations and assign a configurations dictionary to override configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "e6hmQhTw4nks",
        "outputId": "839820be-f5a9-476d-d458-7d2ff3278e89"
      },
      "source": [
        "experiment.configs(conf,\n",
        "                # A dictionary of configurations to override\n",
        "                {'tokenizer': 'character',\n",
        "                'text': 'tiny_shakespeare',\n",
        "                'optimizer.learning_rate': 1.,\n",
        "                'optimizer.optimizer': 'Noam',\n",
        "                'prompt': 'It is',\n",
        "                'prompt_separator': '',\n",
        "\n",
        "                'train_loader': 'sequential_train_loader',\n",
        "                'valid_loader': 'sequential_valid_loader',\n",
        "\n",
        "                'seq_len': 2,\n",
        "                'mem_len': 32,\n",
        "                'epochs': 128,\n",
        "                'batch_size': 32,\n",
        "                'inner_iterations': 25,\n",
        "                })"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvI7MtgJ61w5"
      },
      "source": [
        "Set PyTorch models for loading and saving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "GDlt7dp-5ALt",
        "outputId": "0543a726-fcf4-4493-dbe9-ab62c5bea94f"
      },
      "source": [
        "experiment.add_pytorch_models({'model': conf.model})"
      ],
      "execution_count": 6,
      "outputs": [
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              "<pre style=\"overflow-x: scroll;\">Prepare model...\n",
              "  Prepare n_tokens...\n",
              "    Prepare text...\n",
              "      Prepare tokenizer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t3.81ms</span>\n",
              "      Download<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t133.10ms</span>\n",
              "      Load data<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t7.14ms</span>\n",
              "      Tokenize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t28.06ms</span>\n",
              "      Build vocabulary<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t139.53ms</span>\n",
              "    Prepare text<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t327.93ms</span>\n",
              "  Prepare n_tokens<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t338.87ms</span>\n",
              "  Prepare device...\n",
              "    Prepare device_info<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t61.05ms</span>\n",
              "  Prepare device<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t68.43ms</span>\n",
              "Prepare model<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t10,682.78ms</span>\n",
              "</pre>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJZRf8527GxL"
      },
      "source": [
        "Start the experiment and run the training loop."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "aIAWo7Fw5DR8",
        "outputId": "64764a3f-e8d7-4c06-b803-0da6ff756163"
      },
      "source": [
        "# Start the experiment\n",
        "with experiment.start():\n",
        "    conf.run()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "<pre style=\"overflow-x: scroll;\">\n",
              "<strong><span style=\"text-decoration: underline\">transformer_xl</span></strong>: <span style=\"color: #208FFB\">d3b6760c692e11ebb6a70242ac1c0002</span>\n",
              "\t[dirty]: <strong><span style=\"color: #DDB62B\">\"\"</span></strong>\n",
              "Initialize...\n",
              "  Prepare mode<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t6.08ms</span>\n",
              "Initialize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t91.71ms</span>\n",
              "Prepare validator...\n",
              "  Prepare valid_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t72.62ms</span>\n",
              "<span style=\"color: #C5C1B4\"></span>\n",
              "<span style=\"color: #C5C1B4\">--------------------------------------------------</span><span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>\n",
              "<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\">LABML WARNING</span></strong></span>\n",
              "<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>LabML App Warning: <span style=\"color: #60C6C8\">empty_token: </span><strong>Please create a valid token at https://app.labml.ai.</strong>\n",
              "<strong>Click on the experiment link to monitor the experiment and add it to your experiments list.</strong><span style=\"color: #C5C1B4\"></span>\n",
              "<span style=\"color: #C5C1B4\">--------------------------------------------------</span>\n",
              "<span style=\"color: #208FFB\">Monitor experiment at </span><a href='https://app.labml.ai/run?uuid=d3b6760c692e11ebb6a70242ac1c0002' target='blank'>https://app.labml.ai/run?uuid=d3b6760c692e11ebb6a70242ac1c0002</a>\n",
              "Prepare validator<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t159.97ms</span>\n",
              "Prepare trainer...\n",
              "  Prepare train_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t105.71ms</span>\n",
              "Prepare trainer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t138.48ms</span>\n",
              "Prepare training_loop...\n",
              "  Prepare loop_count<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t34.64ms</span>\n",
              "Prepare training_loop<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t276.11ms</span>\n",
              "<span style=\"color: #C5C1B4\">It is</span><strong>m</strong><strong>D</strong><strong>$</strong><strong>I</strong><strong>m</strong><strong>?</strong><strong>P</strong><strong>h</strong><strong>g</strong><strong>p</strong><strong>Q</strong><strong>x</strong><strong>P</strong><strong>P</strong><strong>P</strong><strong>P</strong><strong>,</strong><strong>o</strong><strong>F</strong><strong>r</strong><strong>F</strong><strong>r</strong><strong>F</strong><strong>r</strong><strong>F</strong>\n",
              "<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong>\n",
              "<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>t</strong>\n",
              "<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>d</strong><strong>,</strong><strong></strong>\n",
              "<strong></strong><strong>T</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>w</strong><strong>h</strong><strong>i</strong><strong>s</strong><strong> </strong><strong>w</strong><strong>i</strong><strong>t</strong><strong>h</strong><strong> </strong><strong>w</strong><strong>e</strong><strong> </strong><strong>w</strong><strong>i</strong><strong>t</strong><strong>h</strong>\n",
              "<span style=\"color: #C5C1B4\">It is</span><strong>e</strong><strong>n</strong><strong>t</strong><strong>e</strong><strong>r</strong><strong> </strong><strong>h</strong><strong>e</strong><strong>a</strong><strong>v</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>m</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>a</strong><strong>n</strong>\n",
              "<strong><span style=\"color: #DDB62B\"> 200,768:  </span></strong>Sample:<span style=\"color: #C5C1B4\"> 100%</span><span style=\"color: #208FFB\">   393ms  </span>Train:<span style=\"color: #C5C1B4\">  20%</span><span style=\"color: #208FFB\">  1,161,309ms  </span>Valid:<span style=\"color: #C5C1B4\">  16%</span><span style=\"color: #208FFB\"> 32,270ms  </span> loss.train: <span style=\"color: #C5C1B4\"> 2.12580</span> accuracy.train: <span style=\"color: #C5C1B4\">0.335542</span> loss.valid: <strong> 2.23436</strong> accuracy.valid: <strong>0.326474</strong>  <span style=\"color: #208FFB\">1,193,972ms</span><span style=\"color: #D160C4\">  0:04m/ 42:23m  </span></pre>"
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      "execution_count": null,
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